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A Smart Recycling Bin Using Waste Image Classification At The Edge
The model reached an accuracy of 95. 98% on Jetson Nano and 96. 64% on K210.
Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis
The paradigm of automated waste classification has recently seen a shift in the domain of interest from conventional image processing techniques to powerful computer vision algorithms known as convolutional neural networks (CNN).
Towards artificially intelligent recycling Improving image processing for waste classification
The work reported in this paper builds on this project through the use of transfer learning and data augmentation techniques to ameliorate classification accuracy.
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